Hyperparameter Tuning

Published 2026-02-10 in Deep Learning

Hyperparameter tuning is the process of selecting the best configuration settings for a machine learning algorithm to improve its performance. Unlike model parameters, hyperparameters are set before training and control how the model learns from data. The table highlights commonly used machine learning algorithms and their key hyperparameters. 1. Linear Regression Key Hyperparameter: Regularization parameter (α for Ridge/Lasso) Regularization controls model complexity by penalizing large coefficients. A higher value of α reduces overfitting, while a lower value allows the model to fit the data more closely. 2. Logistic Regression Key Hyperparameters: C (inverse of regularization strength) Penalty (L1, L2) The parameter C controls the trade-off between fitting the training data and regularization. The penalty type determines how coefficients are constrained, affecting sparsity and generalization. 3. Decision Tree Key Hyperparameters: Max depth min_samples_split min_samples_leaf Criterion These hyperparameters control the size and structure of the tree. Proper tuning helps prevent overfitting while maintaining interpretability. 4. K-Nearest Neighbors (KNN) Key Hyperparameters…

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